Nutritional Status and Productive Components of Maize Fertilized With Sewage Sludge
Bibliographic record
Abstract
The response of crops to the application of biofertilizers in the soil is not yet well established. Therefore, we carried out this study with the objective of evaluating the productive potential of maize fertilized with doses of sewage sludge, comparing with the use of mineral fertilizer over four agricultural seasons. The experiment was conducted in a Red Dystroferric Nitosol (Ultisol) in a randomized block design and repeated measures in time. The treatments consisted of five doses of sewage sludge and one treatment using nitrogen, phosphorus and potassium in formulation. All fertilizations were carried out at maize sowing at four planting times (summer and 2nd crop of 1st agricultural year and, summer and 2nd crop of 2nd agricultural year) and replicates. The results showed a cumulative increase in the P, Ca, Mg and Zn leaf concentrations over the four seasons crops, when the sewage sludge was used in the 30 and 40 Mg ha-1 doses. However, reductions in plant height, weight and maize cob diameter, mass and grain yield were observed at the end of the last harvest. The treated sewage sludge is a viable alternative for maize fertilization in the first harvest, provides increases in grain yield in the order of 1.35 Mg ha-1 and can be recommended instead of NPK mineral fertilizers in built-up fertility soils.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".